Continuing Professional Development

Data Visualisation and Predictive Modelling Using R


The details
Data visualisation and predictive modelling using R
30 credits
Level 6
No date available
Colchester Campus

We are not currently accepting applications for this course.

This new short course is part of an Office for Students (OfS) funded pilot project to explore flexible courses that expand digital skillsets in data science and analysis.

There are two components to this course. Both require previous knowledge of R, and the second component will require a significant statistical background which the student is either expected to possess already or be capable of acquiring independently through the strength of prior studies or aptitude.

The course “Introduction to statistics and data science with R” is a suitable prerequisite.

Component 1 - Visualisation in R

The objective of this component is to identify patterns and display information from data from several sources; to encourage statistical thinking by a series of examples of good and not-so-good visualisations and guide students in their development of creativity within a scientific framework.

This component will highlight how visualisation plays a key role in many disciplines.

Component 2 - Predictive modelling

This component will introduce the principles for the application of linear modelling and machine learning methodologies for the analysis of experimental and observational data. R is used throughout.

The first strand of this component will study regression models, variable selection and ANOVA. The second strand will introduce several machine learning techniques. The third strand of the module will study techniques for dealing with missing data.

Learning outcomes

By the end of the course, you will be able to demonstrate the following:

  1. Summarise and understand information on categorical and continuous variables
  2. Explore relationships between different variables
  3. Display graphical information and complex relationships in datasets using R
  4. Use advanced statistical packages like ggplot2 and produce statistical reports with Rmarkdown
  5. Create interactive plots.
  6. Use the module statistical methods and techniques to review, consolidate, extend and apply knowledge.
  7. Critically evaluate the performance of the Data analysis methods studied.
  8. Familiarity with a general linear model using real data;
  9. Familiarity with assessing fitted models and validating linear model assumptions;
  10. Ability to identify and conduct simple designed experiments;
  11. Familiarity with the construction of factorial experiments in blocks;
  12. Ability to employ and assess the results of discriminant analysis, multiple logistic regression, principal component, clustering and multivariate analysis of variance with real observational data.
  13. Communicate via data visualisation and modelling results to both specialist and non-specialist audiences.

Entry Requirements

Please note that we are not currently accepting applications for this course.

Our short courses are designed to be accessible to all. There are no specific entry requirements you need to meet, and you do not need a background in mathematics or computer science to be eligible for this course.

You may find the course Introduction to statistics and data science with R helpful, but it is not a requirement for this course.


Module Outline

The purpose of this module is to introduce:

  • Predictive modelling using R
  • The use of R for data visualisation.

The module will be delivered over a 13-week period. This will consist of 10 hours of online lectures for each of the two components, totalling 20 hours, which will be broken into bite size chunks.

In addition, there will be 10 hours of face-to-face labs on campus for each component, totalling 20 hours of face-to-face labs.

Participants will also need to carry out independent work such as practicing skills and reading in advance of lectures and labs.

Teaching schedule

Monday 2 January 2023
10 – 1pm and 2 – 4pm – teaching over zoom

Tuesday 3 January 2023
10 – 12pm and 1 – 4pm – IT Labs on campus

Monday 9 January 2023
10 – 1pm and 2 – 4pm – teaching over zoom

Tuesday 10 January 2023
10 – 12pm and 1 – 4pm – IT Labs on campus

Thursday 12 January 2023
10 – 1pm – teaching over zoom

Friday 13 January 2023
10 – 12pm – IT labs on campus

Monday 27 March 2023
10 – 12pm and 1 – 4pm – teaching over zoom

Tuesday 28 March 2023
10 – 1pm and 2 – 4pm – IT labs on campus

Thursday 30 March 2023
10 – 12pm – teaching over zoom

Friday 31 March 2023
10 – 1pm – IT labs on campus

Please note dates and times may be subject to change.

Assessment strategy

Assessment will consist of a report of no more than 10 pages, and a related Rmarkdown.

In addition, participants will deliver a short presentation to describe their findings.

Fees and funding

The course costs £2,310. This includes all lectures, labs and assessment costs, and access to university facilities such as the library, the Silberrad Student Centre and student support services.

Funding for this course can be applied for through Student Finance Higher Education Short Course Loans.

What's next

All applications for this course can be made online.
For further help or information, please contact